我这里仅显示道路和车道线
1 mask = np.zeros((hh, ww, 3), dtype=np.uint8) 2 mask[np.where(res > 0)] = (0, 255, 0) # 路面 3 mask[np.where(res > 1)] = (255, 0, 0) # 车道线
模型的下载还是老方法
Accuracy
The quality metrics calculated on 500 images from "Mighty AI" dataset that was converted for four class classification task are:
Label | IOU | ACC |
---|---|---|
mean | 0.844 | 0.901 |
BG | 0.986 | 0.994 |
road | 0.954 | 0.974 |
curbs | 0.727 | 0.831 |
marks | 0.708 | 0.806 |
IOU=TP/(TP+FN+FP)
ACC=TP/GT
TP
- number of true positive pixels for given classFN
- number of false negative pixels for given classFP
- number of false positive pixels for given classGT
- number of ground truth pixels for given class
Performance
Inputs
A blob with a BGR image in the format: [B, C=3, H=512, W=896], where:
- B – batch size
- C – number of channels
- H – image height
- W – image width
Outputs
The output is a blob with the shape [B, C=4, H=512, W=896]. It can be treated as a four-channel feature map, where each channel is a probability of one of the classes: BG, road, curb, mark.
1 import time 2 import cv2 as cv 3 import numpy as np 4 import numpy as py 5 from openvino.inference_engine import IECore 6 7 gengers = ["femal", "male"] 8 9 def face_landmarks35(): 10 ie = IECore() 11 for device in ie.available_devices: 12 print(device) 13 model_xml = "road-segmentation-adas-0001.xml" 14 model_bin = "road-segmentation-adas-0001.bin" 15 net = ie.read_network(model=model_xml, weights=model_bin) 16 input_blob = next(iter(net.input_info)) 17 out_blob = next(iter(net.outputs)) 18 n, c, h, w = net.input_info[input_blob].input_data.shape 19 print(n, c, h, w) # 1 3 384 384 20 21 cap = cv.VideoCapture("2.mp4") 22 exec_net = ie.load_network(network=net, device_name="CPU") 23 24 while True: 25 ret, frame = cap.read() 26 if ret is not True: 27 break 28 image = cv.resize(frame, (w, h)) 29 image = image.transpose(2, 0, 1) 30 inf_start = time.time() 31 # 路面与车道线分割 32 res = exec_net.infer(inputs={input_blob: [image]}) 33 inf_end = time.time() - inf_start 34 ih, iw, ic = frame.shape 35 res = res[out_blob] # (1, 4, 512, 896) 36 res = np.squeeze(res, 0) # (4, 512, 896) 37 #res = res.transpose(1, 2, 0) # (512, 896, 4) 38 res = np.argmax(res, 0) 39 hh, ww = res.shape 40 mask = np.zeros((hh, ww, 3), dtype=np.uint8) 41 mask[np.where(res > 0)] = (0, 255, 0) # 路面 42 mask[np.where(res > 1)] = (255, 0, 0) # 车道线 43 mask = cv.resize(mask, (frame.shape[1], frame.shape[0])) 44 result = cv.addWeighted(frame, 0.5, mask, 0.5, 0) 45 46 cv.imshow("road segment", result) 47 cv.waitKey(1) 48 cv.destroyAllWindows() 49 50 51 if __name__ == "__main__": 52 face_landmarks35()View Code
标签:openvino,res,mask,分割,道路,blob,np,net,cv From: https://www.cnblogs.com/feiyull/p/15471605.html